ArticlePloS one2023
Deep audio embeddings for vocalisation clustering.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 29 citations in OpenAlex.
- Investigating the structure of the greylag goose vocal repertoire: what can unsupervised methods tell us?Scientific reports · 2026Article
- TweetyBERT: Automated parsing of birdsong through self-supervised machine learning.Patterns (New York, N.Y.) · 2026Article
- Contrastive learning for passive acoustic monitoring: A framework for sound source discovery and cross-site comparison in marine soundscapes.PLoS computational biology · 2026Article
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- Identification and characterization of clusters of potentially new vocalizations in broiler chickens using advanced acoustic analysis.Poultry science · 2025Article
- Roar Data: Redefining a Lion's Roar Using Machine Learning.Ecology and evolution · 2025Article
- A large annotated dataset of vocalizations by common marmosets.Scientific data · 2025Article
- Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Visualization and quantification of coral reef soundscapes using CoralSoundExplorer software.PLoS computational biology · 2025Article
- Bioacoustic fundamental frequency estimation: a cross-species dataset and deep learning baseline.Bioacoustics · 2025Article
- A first vocal repertoire characterization of long-finned pilot whales (Royal Society open science · 2024Article
- SqueakOut: Autoencoder-based segmentation of mouse ultrasonic vocalizations.bioRxiv : the preprint server for biology · 2024Article
- Soundscape Characterization Using Autoencoders and Unsupervised Learning.Sensors (Basel, Switzerland) · 2024Article
- Vocal complexity in a socially complex corvid: gradation, diversity and lack of common call repertoire in male rooks.Royal Society open science · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of non-human animals' communication systems generally relies on the transcription of vocal sequences using a finite set of discrete units. This set is referred to as a vocal repertoire, which is specific to a species or a sub-group of a species. When conducted by human experts, the formal description of vocal repertoires can be laborious and/or biased. This motivates computerised assistance for this procedure, for which machine learning algorithms represent a good opportunity. Unsupervised clustering algorithms are suited for grouping close points together, provided a relevant representation. This paper therefore studies a new method for encoding vocalisations, allowing for automatic clustering to alleviate vocal repertoire characterisation. Borrowing from deep representation learning, we use a convolutional auto-encoder network to learn an abstract representation of vocalisations. We report on the quality of the learnt representation, as well as of state of the art methods, by quantifying their agreement with expert labelled vocalisation types from 8 datasets of other studies across 6 species (birds and marine mammals). With this benchmark, we demonstrate that using auto-encoders improves the relevance of vocalisation representation which serves repertoire characterisation using a very limited number of settings. We also publish a Python package for the bioacoustic community to train their own vocalisation auto-encoders or use a pretrained encoder to browse vocal repertoires and ease unit wise annotation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.